Package 'pamm'

number of simulation for each step

number of group

number of replicates per group

vector of lenght 3 with mean, variance and estimate of fixed effect to simulate. Default: c(0, 1, 0)

variance component of intercept. Could be specified as a vector. Default: seq(0.05, 0.95, 0.05)

Variance component of the slope or IxE. Could be specified as a vector. Default: seq(0.05, 0.5, 0.05)

value of correlation or covariance between random intercept and random slope. Default: 0

"cor" or "cov" set the type of relation give in CoIS. By default the relation is set to correlation

number of different values to simulate for the fixed effect (covariate). If NA, all values of X are independent between groups. If the value specified is equivalent to the number of replicates per group, repl, then all groups are observed for the same values of the covariate. Default: NA

correlation between two successive covariate value for a group. Default: 0

specify the distribution of the fixed effect. Only "gaussian" (normal distribution) and "unif" (uniform distribution) are accepted actually. Default: "gaussian"

a numeric value giving the expected intercept value. Default: 0

a vector specifying heterogeneity in residual variance across X. If c("null") residual variance is homogeneous across X. If c("power",t1,t2) models heterogeneity with a constant plus power variance function. Letting $v$ denote the variance covariate and $\sigma^2(v)$ denote the variance function evaluated at $v$, the constant plus power variance function is defined as $\sigma^2(v) = (\theta_1 + |v|^{\theta_2})^2$, where $\theta_1,\theta_2$ are the variance function coefficients. If c("exp",t),models heterogeneity with an exponential variance function. Letting $v$ denote the variance covariate and $\sigma^2(v)$ denote the variance function evaluated at $v$, the exponential variance function is defined as $\sigma^2(v) = e^{2 * \theta * v}$, where $\theta$ is the variance function coefficient. Default:"Null"

Use the simluate.merMod function to simulate the data. Potentially faster for large dataset but more restricted in terms of options

Simulate the data based on a existing data and model structure from a lmer object. Should be specified as a list of 3 components: a mer object fitted via lmer, an environmental covariate for which to test the random slope, a random effect (e.g. list(fm1,"Days","Subject")

number of simulation for each step

number of group. Could be specified as a vector

number of replicates per group . Could be specified as a vector

vector of lenght 4 or 5, with 1: variance component of intercept, VI; 2: variance component of slope, VS; 3: residual variance, VR; 4: relation between random intercept and random slope; 5: "cor" or "cov" determine if the relation 4 between I ans S is a correlation or a covariance. Default: "cor"

vector with mean, variance and estimate of fixed effect to simulate. Default: c(0, 1, 0)

number of different values to simulate for the fixed effect (covariate). If NA, all values of X are independent between groups. If the value specified is equivalent to the number of replicates per group, repl, then all groups are observed for the same values of the covariate. Default: NA

correlation between two successive covariate value for a group. Default: 0

specify the distribution of the fixed effect. Only "gaussian" (normal distribution) and "unif" (uniform distribution) are accepted actually. Default: "gaussian"

a numeric value giving the expected intercept value. Default:0

a vector specifying heterogeneity in residual variance across X. If c("null") residual variance is homogeneous across X. If c("power",t1,t2) models heterogeneity with a constant plus power variance function. Letting $v$ denote the variance covariate and $\sigma^2(v)$ denote the variance function evaluated at $v$, the constant plus power variance function is defined as $\sigma^2(v) = (\theta_1 + |v|^{\theta_2})^2$, where $\theta_1,\theta_2$ are the variance function coefficients. If c("exp",t),models heterogeneity with an exponential variance function. Letting $v$ denote the variance covariate and $\sigma^2(v)$ denote the variance function evaluated at $v$, the exponential variance function is defined as $\sigma^2(v) = e^{2 * \theta * v}$, where $\theta$ is the variance function coefficient.

character value "lmer", "lme" or "MCMCglmm" specifying the function to use to fit the model. Actually "lmer" only is accepted

simulate the data using simulate.merMod from lme4. Faster for large sample size but not as flexible.

an EAMM object

"VI", "VS", or "both" "VI" give graphs with varying variance component of intercept and with a fixed variance component for slope specified in vs argument "VS" give graphs with varying variance component for slope and with a fixed variance component of intercept specified in vi argument "both" 3-D plot see also fun3D argument

VI for which plots the output. Necessary for "VS" type of graph

VS for which plots the output. Necessary for "VI" type of graph

plot function used to plot the 3D graph. "wireframe" uses lattice, "persp" uses graphics, and "open3d" uses rgl.

potentially further arguments to pass to methods

a PAMM object

"group", "repl" or "both" "group" give graphs with varying number of ID and with a fixed number of replicates specified in nbrepl "repl" give graphs with varying number of replicates and with a fixed number of ID specified in nbgroup "both" 3-D plot. see also fun3D argument. Note: useful only with multiple group size and multiple number of replicates.

number of group for which plots the output. Necessary for "repl" type of graph

number of replicates for which plots the output. Necessary for "group" type of graph

plot function used to plot the 3D graph. "wireframe" uses lattice, "persp" uses graphics and "open3d" uses rgl

potentially further arguments to pass to methods

an SSF object

potentially further arguments to pass to methods

number of simulation for each step

total sample size, nb group * nb replicates

number of group*replicates associations to simulate

vector of lenght 4 or 5 with 1: variance component of intercept, VI; 2: variance component of slope, VS; 3: residual variance, VR; 4: relation between random intercept and random slope; 5: "cor" or "cov" determine id the relation between I ans S is correlation or covariance, set to "cor" by default

vector of lenght 3 with mean, variance and estimate of fixed effect to simulate

number of different values to simulate for the fixed effect (covariate). If NA, all values of X are independent between groups. If the value specified is equivalent to the number of replicates per group, repl, then all groups are observed for the same values of the covariate. Default: NA

correlation between two successive covariate value for a group. Default: 0

specify the distribution of the fixed effect. Only "gaussian" (normal distribution) and "unif" (uniform distribution) are accepted actually. Default: "gaussian"

a numeric value giving the expected intercept value. Default:0

a vector specifying minimum and maximum value for number of group. Default:c(2,tss/2)

a vector specifying minimum and maximum value for number of replicates. Default:c(2,tss/2)

a vector specifying heterogeneity in residual variance across X. If c("null") residual variance is homogeneous across X. If c("power",t1,t2) models heterogeneity with a constant plus power variance function. Letting $v$ denote the variance covariate and $\sigma^2(v)s2(v)$ denote the variance function evaluated at $v$, the constant plus power variance function is defined as $\sigma^2(v) = (\theta_1 + |v|^{\theta_2})^2s2(v) = (t1 + |v|^t2)^2$, where $\theta_1,\theta_2t1, t2$ are the variance function coefficients. If c("exp",t),models heterogeneity with an exponential variance function. Letting $v$ denote the variance covariate and $\sigma^2(v)s2(v)$ denote the variance function evaluated at $v$, the exponential variance function is defined as $\sigma^2(v) = e^{2 * \theta * v}s2(v) = exp(2* t * v)$, where $\thetat$ is the variance function coefficient.